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Find studies

Showing 1–21 of 21 studies with an author at University of Pennsylvania (US) · free to read · tagged Digital Tools · 30,980 in the whole library · How the 0–100 score works

Download bibliography of these 21 studies, in this order: BibTeX (.bib) · RIS (.ris)

Browse by subject, author or institution

Each number is how many studies that link shows, with your other filters kept.

A note on reading research. One study is rarely the final word.

We try to note the size of a study, whether it was in humans or animals, and how early-stage the finding is. If a summary here doesn't make that clear, treat it with extra caution and check the original source.

Predicting Alzheimer's disease progression using multi-modal deep learning approach

✓ Peer-reviewed alzhub usefulness score 66 out of 100. Scientific reports 2019-02-13

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. 1 key finding →

Design: cross-sectional Evidence: moderate

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. 1 key finding →

Design: cohort study Participants: 37,096 Evidence: moderate

One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. 1 key finding →

Design: longitudinal study Evidence: moderate

The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). 2 key findings →

Participants: 2,146 Evidence: low

Early detection of Alzheimer's disease (AD) is critical yet challenging, particularly in younger individuals. 1 key finding →

Design: longitudinal study Participants: 120 Evidence: moderate

Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer's disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. 5 key findings →

Design: cross-sectional Participants: 568 Evidence: moderate

New personalized endpoints that are clinically meaningful to individuals and their families should be developed. 1 key finding →

Evidence: low

Divergent patterns of TDP-43 and tau pathologies in primary progressive aphasia

✓ Peer-reviewed alzhub usefulness score 48 out of 100. Annals of neurology 2019-03-28

Postmortem cortical pathology was left-lateralized in both FTLD-TDP (beta = -0.15, standard error [SE] = 0.05, p = 0.007) and FTLD-Tau (beta = -0.09, SE = 0.04, p = 0.015), but the degree of lateralization decreased with greater overall dementia severity before death (beta = -8.18, SE = 3.22, p = 0.015). 2 key findings →

Evidence: moderate

The pathological hallmark of neurodegenerative diseases is the formation of toxic oligomers by proteins such as alpha-synuclein (aSyn) or microtubule-associated protein tau (Tau). 4 key findings →

Evidence: low

BackgroundLet's Talk Tech (LTT) is a self-administered web intervention for people with memory loss and their care partners that supports decision-making about digital health technologies. 5 key findings →

Participants: 15 Evidence: low

Of 15,390 narratives collected from 3139 patients with PsA for exploratory analysis, physical concepts were the most common (81.5%), followed by emotional (50.7%), cognitive (20.0%), role activity (8.1%), and social (5.6%) concepts. 5 key findings →

Participants: 3,139 Evidence: low

(n = 14) included NH administrators and other leaders, palliative care providers, telehealth representatives, dementia advocates, a care partner, and a PLWD. 5 key findings →

Participants: 14 Evidence: low

This study constitutes both a scoping review and a concept analysis, as it systematically examines digital literacy across settings (home, assisted living facilities, and other long-term care facilities) in the context of persons with dementia and their caregivers. 4 key findings →

Evidence: low

Addressing disparities related to Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) is a priority for policymakers, practitioners, and researchers. 5 key findings →

Evidence: low

Participants described AI tools as potentially helpful for obtaining information, monitoring patients, managing medications and appointments, providing companionship, and conducting logistical tasks. 1 key finding →

Participants: 30 Evidence: low

Artificial intelligence (AI) has gained attention as a tool to support dementia care through efficient and personalized interventions. 5 key findings →

Participants: 19 Evidence: low

Detecting Self-Repairs from Spontaneous Speech with Prompt Ablation Across LLMs and Fine-Tuned Encoder

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-08-25

Self-repairs - in-utterance revisions in which a speaker abandons and reformulates their speech - are a promising interpretable marker for speech-based cognitive screening. 1 key finding →

Evidence: low

Smartphone Passive Digital Phenotyping in Frontotemporal Lobar Degeneration

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-09-21

IntroductionFrontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. 2 key findings →

Design: longitudinal study Participants: 567 Evidence: moderate

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Where “Free full text” comes from

Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled.

How the 0–100 score works — and why some entries are capped

This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.

  • Journal tier up to 10 pts
  • Journal's citation record up to 4 pts
  • Study design up to 12 pts
  • Strength of evidence up to 5 pts
  • Peer-review validation up to 10 pts
  • How recent up to 5 pts
  • Sample size up to 4 pts
  • Effect reported up to 3 pts
  • Studied in people up to 3 pts
  • Randomised / blinded up to 2 pts
  • Times cited up to 7 pts
  • Citations per year up to 5 pts
  • Impact for its field and year up to 6 pts
  • Cited by studies here up to 6 pts
  • Still being cited up to 4 pts
  • Senior author's track record up to 4 pts
  • Institution's track record up to 3 pts
  • Multi-centre collaboration up to 3 pts
  • Indexed in PubMed up to 2 pts
  • Free to read up to 2 pts

Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.

Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.

We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.

The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.

See the whole rubric drawn out → — four charts covering all 30,980 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.

What this library is — and isn't

This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 30,980 records, built mostly from automated pulls of Europe PMC (65.2% of the library) and OpenAlex (16.4%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.

1,040 records (3.4% of the library) are not peer-reviewed — 965 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.

See the full, machine-readable breakdown of this library's coverage →

Newest studies, as a dated timeline →    Atom feed    Where this research is being discussed: events